scholarly journals Disentangling Pro-mitotic Signaling during Cell Cycle Progression using Time-Resolved Single-Cell Imaging

Cell Reports ◽  
2020 ◽  
Vol 31 (2) ◽  
pp. 107514 ◽  
Author(s):  
Manuela Benary ◽  
Stefan Bohn ◽  
Mareen Lüthen ◽  
Ilias K. Nolis ◽  
Nils Blüthgen ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Cell Imaging ◽  
Cycle Progression ◽  
Time Resolved ◽  
Single Cell Imaging

scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2019 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Cell Types ◽  
Cell Cycle Phase ◽  
Cellular Heterogeneity ◽  
Cycle Phase ◽  
Cycle Progression ◽  
Cellular Environment

AbstractCellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

scholarly journals Single-cell intracellular pH measurements reveal cell-cycle linked pH dynamics

2021 ◽  
Author(s):  
Julia S Spear ◽  
Katharine A White
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Intracellular Ph ◽  
Cell Cycle Progression ◽  
Single Cells ◽  
Population Level ◽  
Growth Factor Signaling ◽  
Cycle Progression ◽  
Cell Behaviors ◽  
Sinusoidal Pattern

Transient changes in intracellular pH (pHi) have been shown to regulate normal cell behaviors like migration and cell-cycle progression, while dysregulated pHi dynamics are a hallmark of cancer. However, little is known about how pHi heterogeneity and dynamics influence population-level measurements or single-cell behaviors. Here, we present and characterize single-cell pHi heterogeneity distributions in both normal and cancer cells and measure dynamic pHi increases in single cells in response to growth factor signaling. Next, we measure pHi dynamics in single cells during cell cycle progression. We determined that single-cell pHi is significantly decreased at the G1/S boundary, increases from S phase to the G2/M transition, rapidly acidifies during mitosis, and recovers in daughter cells. This sinusoidal pattern of pHi dynamics was linked to cell cycle timing regardless of synchronization method. This work confirms prior work at the population level and reveals distinct advantages of single-cell pHi measurements in capturing pHi heterogeneity across a population and dynamics within single cells.

scholarly journals Predicting Individual Cell Division Events from Single-Cell ERK and Akt Dynamics

2021 ◽  
Author(s):  
Alan D Stern ◽  
Gregory R Smith ◽  
Luis C Santos ◽  
Deepraj Sarmah ◽  
Xiang Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Division ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Time Window ◽  
Single Cells ◽  
Machine Learning Algorithms ◽  
Cycle Progression ◽  
Restriction Point ◽  
Activity Dynamics

Predictive determinants of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. What drives proliferation decisions of single cells at any given time? We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window (~8.5-40 hours). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Machine learning algorithms show that simple metrics of central tendency in this time window are most predictive for subsequent cell division; median ERK and Akt activities classify individual division events with an AUC=0.76. Surprisingly, ERK dynamics alone predict division in individual cells with an AUC=0.74, suggesting Akt activity dynamics contribute little to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics are substantially more important than Akt activity dynamics for driving cell division in this non-transformed context. Single cell imaging along with machine learning algorithms provide a better basis to understand cell cycle progression on the single cell level.

scholarly journals Functional oscillation of a multienzyme glucosome assembly during cell cycle progression

2022 ◽  
Author(s):  
Miji Jeon ◽  
Danielle L Schmitt ◽  
Minjoung Kyoung ◽  
Songon An
Keyword(s):  
Cell Cycle ◽  
Glucose Metabolism ◽  
Single Cell ◽  
Cell Biology ◽  
Cell Cycle Progression ◽  
Metabolic Adaptation ◽  
Dependent Manner ◽  
Cycle Progression ◽  
Size Dependent ◽  
A Cell

Glucose metabolism has been studied extensively to understand functional interplays between metabolism and a cell cycle. However, our understanding of cell cycle-dependent metabolic adaptation particularly in human cells remains largely elusive. Meanwhile, human enzymes in glucose metabolism are shown to functionally organize into three different sizes of a multienzyme metabolic assembly, the glucosome, to regulate glucose flux in a size-dependent manner. Here, using fluorescence single-cell imaging techniques, we discover that glucosomes spatiotemporally oscillate during a cell cycle in an assembly size-dependent manner. Importantly, their oscillation at single-cell levels is in accordance with functional contributions of glucose metabolism to cell cycle progression at a population level. Collectively, we demonstrate functional oscillation of glucosomes during cell cycle progression and thus their biological significance to human cell biology.

scholarly journals Single cell transcriptomics and developmental trajectories of murine cranial neural crest cell fate determination and cell cycle progression

2021 ◽  
Author(s):  
Yu Ji ◽  
Shuwen Zhang ◽  
Kurt Reynolds ◽  
Ran Gu ◽  
Moira McMahon ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Neural Crest ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Cycle Progression ◽  
Developmental Trajectories ◽  
Cell Fate Determination ◽  
Cycle Progression ◽  
Fate Determination ◽  
Neural Lineage

Cranial neural crest (NC) cells migrate long distances to populate the future craniofacial regions and give rise to various tissues, including facial cartilage, bones, connective tissues, and cranial nerves. However, the mechanism that drives the fate determination of cranial NC cells remains unclear. Using single-cell RNA sequencing combined genetic fate mapping, we reconstructed developmental trajectories of cranial NC cells, and traced their differentiation in mouse embryos. We identified four major cranial NC cell lineages at different status: pre-epithelial-mesenchymal transition, early migration, NC-derived mesenchymal cells, and neural lineage cells from embryonic days 9.5 to 12.5. During migration, the first cell fate determination separates cranial sensory ganglia, the second generates mesenchymal progenitors, and the third separates other neural lineage cells. We then focused on the early facial prominences that appear to be built by undifferentiated, fast-dividing NC cells that possess similar transcriptomic landscapes, which could be the drive for the facial developmental robustness. The post-migratory cranial NC cells exit the cell cycle around embryonic day 11.5 after facial shaping is completed and initiates further fate determination and differentiation processes. Our results demonstrate the transcriptomic landscapes during dynamic cell fate determination and cell cycle progression of cranial NC lineage cells and also suggest that the transcriptomic regulation of the balance between proliferation and differentiation of the post-migratory cranial NC cells can be a key for building up unique facial structures in vertebrates.

Abstract 278: Metabolic Reprogramming is Essential for Cell Cycle Progression in Cardiomyocytes

2020 ◽  
Vol 127 (Suppl_1) ◽  
Author(s):  
Riham Abouleisa ◽  
Lindsey McNally ◽  
Qinghui Ou ◽  
Krishna Choudhary ◽  
Reuben Thomas ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Division ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Biosynthetic Pathway ◽  
Cyclin B1 ◽  
Metabolic Reprogramming ◽  
Cycle Progression ◽  
Pathway Activity ◽  
Nad Synthesis

Myocardial infarction causes irreversible loss of cardiomyocytes (CMs) and often leads to heart failure. To replace the lost cells, we identified a combination of cell-cycle regulators that induces stable cytokinesis in adult post-mitotic cells. Specifically, adenoviral overexpression of cyclin-dependent kinase 1 (CDK1), CDK4, cyclin B1, and cyclin D1 (collectively known as ‘four factors’, or simply 4F) induced cell division in ~15% of post-mitotic mouse, rat, and human CMs. Identifying the major roadblocks during the process of CM proliferation is a key for advancing this field. This was not possible before due to the lack of efficient methods to induce CM proliferation. The goal of the current study is to understand why a subpopulation of CMs divide while most CMs, despite expressing 4F, resist cell cycle reentry. To investigate transcriptional changes during cell cycle progression at the single cell level, we conducted temporal single cell RNAseq on 60-day-old matured hiPS-CMs infected with either LacZ (control) or 4F for 24, 48 and 72 h. We found a unique cell population that appears 48 h after infection with the 4F; this population was identified as the proliferating population and expressed high levels of cytokinesis genes (Ki67, Aurora Kinase A and B, E2F1, CDC20, ANLN, TK1, CCNA2, PLK1 and PCNA). Consistent with our published data, this population represents ~15% of the total CMs population expressing 4F. Compared with the quiescent population from the same sample, this unique population of proliferating CMs shows significant upregulation of the cell cycle program and major downregulation of mitochondrial electron transport chain genes. In line with these transcriptomic changes, hiPS-CMs had 50% lower rates of oxidative phosphorylation 48 h after 4F infection. Furthermore, in 4F-overexpressing iPS-CMs, stable isotope tracing demonstrated significantly higher enrichment of glucose-derived 13 C in NAD and UDP-HexNAc, suggesting activation of NAD synthesis and the hexosamine biosynthetic pathway. We conclude that proliferating CMs diminish catabolic pathway activity and augment biosynthetic pathway activity. The capacity of CM subpopulation to reprogram their metabolism is likely to facilitate their ability to complete cell division.

scholarly journals Single-cell transcriptomic analysis of bloodstream Trypanosoma brucei reconstructs cell cycle progression and developmental quorum sensing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Emma M. Briggs ◽  
Federico Rojas ◽  
Richard McCulloch ◽  
Keith R. Matthews ◽  
Thomas D. Otto
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Trypanosoma Brucei ◽  
Cell Cycle Progression ◽  
Expression Patterns ◽  
Tsetse Fly ◽  
Cycle Progression ◽  
Progressive Development ◽  
Asynchronous Development

AbstractDevelopmental steps in the trypanosome life-cycle involve transition between replicative and non-replicative forms specialised for survival in, and transmission between, mammalian and tsetse fly hosts. Here, using oligopeptide-induced differentiation in vitro, we model the progressive development of replicative ‘slender’ to transmissible ‘stumpy’ bloodstream form Trypanosoma brucei and capture the transcriptomes of 8,599 parasites using single cell transcriptomics (scRNA-seq). Using this framework, we detail the relative order of biological events during asynchronous development, profile dynamic gene expression patterns and identify putative regulators. We additionally map the cell cycle of proliferating parasites and position stumpy cell-cycle exit at early G1 before progression to a distinct G0 state. A null mutant for one transiently elevated developmental regulator, ZC3H20 is further analysed by scRNA-seq, identifying its point of failure in the developmental atlas. This approach provides a paradigm for the dissection of differentiation events in parasites, relevant to diverse transitions in pathogen biology.

scholarly journals A novel live-cell imaging system reveals a reversible hydrostatic pressure impact on cell-cycle progression

2018 ◽  
Vol 131 (15) ◽  
pp. jcs212167 ◽  
Author(s):  
Holly R. Brooker ◽  
Irene A. Gyamfi ◽  
Agnieszka Wieckowska ◽  
Nicholas J. Brooks ◽  
Daniel P. Mulvihill ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Hydrostatic Pressure ◽  
Live Cell Imaging ◽  
Cell Cycle Progression ◽  
Cell Imaging ◽  
Imaging System ◽  
Live Cell ◽  
Cycle Progression ◽  
Pressure Impact
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